Interactive Cognition (9/14/2016)

Elements of Interactive Cognition (Perception):

Parallel/Distributed Processing: not clear whether this is for computational advantages or for some other reasons

Formal Models:

Environment:

Labeled examples: a sequence of (X1, l(x1)), … (Xn, l(xn))

Two models:
(a) PAC (probably approximately correct):
Assume distribution D (unknown) on input examples . After seeing m labeled examples (x, l(x)) where x ~ D, a -PAC algorithm finds a hypothesis h with prob. such that:
(on a new input y ~ D, we should be able to guess l(y))
(b) Mistake-bound:
Given an example x, you predict l(x) right away, and are then told the correct answer. The complexity of an algorithm in the mistake-bound model is m, if for any sequence of examples (of any length), the total number of mistakes made by the algorithm is at most m.

Prediction:

Max Likelihood Estimators: find a model that most likely generated the data

Bayesian Estimation: use priors to make decisions

Recurrent (connections):

Recurrent Neural Network (RNN): contains connections that are not feed-forward:

Project Example:

Invariant: Parsing a letter

Environment and Sequential Parsing: I see a letter as a sequence of strokes

Prediction: As someone starts writing a stroke, you can predict which letter it is